Isbn: 9780470919996 - reinforcement and systemic machine learning for decision making (ieee press series on systems science and engineering, band 1) (8 Ergebnisse)

Hardcover. Zustand: Very Good. 1. It's a well-cared-for item that has seen limited use. The item may show minor signs of wear. All the text is legible, with all pages included. It may have slight markings and/or highlighting.

HRD. Zustand: New. New Book. Shipped from UK. Established seller since 2000.

Zustand: New. pp. 312.

Zustand: New. In English.

Zustand: New. Parag Kulkarni, PhD, DSc, is the founder and Chief Scientist of EKLat Research where he has empowered businesses through machine learning, knowledge management, and systemic management. He has been working within the IT industry for over twenty years. The r.

Hardcover. Zustand: Brand New. 1st edition. 312 pages. 9.21x6.14x0.87 inches. In Stock.

Zustand: New. * Authors have both industrial and academic experiences * Case studies are included reflecting author's industrial experiences * Downloadable tutorials are available . Series: IEEE Press Series on Systems Science and Engineering. Num Pages: 312 pages, Illustrations. BIC Classification: UYQM. Category: (P) Professional & Vocational. Dimension: 242 x 164 x 22. Weight in Grams: 582. . 2012. 1st Edition. Hardcover. . . . . Books ship from the US and Ireland. …

Buch. Zustand: Neu. Neuware - Reinforcement and Systemic Machine Learning for Decision MakingThere are always difficulties in making machines that learn from experience. Complete information is not always available--or it becomes available in bits and pieces over a period of time. With respect to systemic learning, there is a need to understand the impact of decisions and actions on a system over that period of time. This book takes a holistic approach to addressing that need and presents a new paradigm--creating new learning applications and, ultimately, more intelligent machines.The first book of its kind in this new and growing field, Reinforcement and Systemic Machine Learning for Decision Making focuses on the specialized research area of machine learning and systemic machine learning. It addresses reinforcement learning and its applications, incremental machine learning, repetitive failure-correction mechanisms, and multiperspective decision making.Chapters include:\* Introduction to Reinforcement and Systemic Machine Learning\* Fundamentals of Whole-System, Systemic, and Multiperspective Machine Learning\* Systemic Machine Learning and Model\* Inference and Information Integration\* Adaptive Learning\* Incremental Learning and Knowledge Representation\* Knowledge Augmentation: A Machine Learning Perspective\* Building a Learning System With the potential of this paradigm to become one of the more utilized in its field, professionals in the area of machine and systemic learning will find this book to be a valuable resource.…